When making automated inter-individual quantitative analysis from PET and SPECT imaging data it is important to have a robust and accurate spatial normalization method, which can be used to transform images from different individuals into a common reference space where comparisons between individuals can be made. This means that different organ images, for example brain images, are transformed into a standard anatomical space in which the organs from different individuals have the same position, size and shape, so as to allow for comparison across different individuals. This is relevant for both volume-of-interest (VOI) analysis as well as voxel-based analysis from imaging data.
For spatial normalization of PET and SPECT images, either the individual PET/SPECT image or a co-registered anatomical image may be used to find the spatial transformation between an individual image and a template image located in the reference space. In most applications, the use of anatomical information will enhance the possibility of making a good spatial normalization. However, there is a trade-off in making a method dependent on an anatomical image. For instance when there is no anatomical image available, the options would either be to disallow the analysis completely, or to use an alternative PET/SPECT-based method which would produce different results.
There is therefore a need in the art for a spatial normalization method specifically for PET and SPECT images, such as images using amyloid imaging agents, which only depends on the PET or SPECT image itself.